Mankind has generated tremendous amounts of digital data, e.g. from cameras, microphones, scientific equipments etc. Out of this wealth of digital data some data is useless to be processed and/or stored whereas other data is of high importance. Digital data such as images, video, audio and text are a part of our collective and individualistic identity and are often used as building blocks for new knowledge, experiences, products, business models, etc. This means that certain data like personal photos can be used as starting points for collective or individualistic applications.
Due to the exponential growth of the availability of digital sensor equipment such as digital cameras, the Internet, mobile phones, etc, the amount of information deemed as important for preservation for either the society in general and each individual person has surpassed all the limitations of human memory, the cataloging systems and even indexing schemes that were in place the last 200 years. The sheer volume of recorded data makes it impossible to locate and retrieve past data unless they are somehow annotated.
The last passage has dire consequences for society and individuals. Existing and new knowledge will be forgotten or rendered useless because there is no economical way for cataloging, organizing and searching it.
The above situation resulted in the emergence of numerous semi-automatic and automatic solutions for media annotation. In such approaches an “intelligent” system tries to substitute a human indexer in assigning annotation tags in objects exemplified by books, photos, mp3s, etc. The success rate of such a scheme depends on the initial assumptions made for the underlying data, the system's scalability capabilities and the quality of the annotation libraries or dictionaries i.e. the actual tags that are used to annotate the data.
Some annotation systems use ontologies, which are formal representations of knowledge as sets of concepts within a specific domain along with the relationships between those concepts. An ontology denotes a taxonomy with a set of inference rules and can be seen as a class hierarchy from abstract to more specific objects. FIG. 1 provides such a taxonomy.
The following are examples of such systems:
US20100030552A1 uses ontologies to describe real world entities and the relationship between tags by determining properties associated with tags and domains, using linguistic analysis.
US20100004923A1 describes a method for ontology-based clustering of process models e.g. manufacturing process in organization. The method involves a distance calculation unit for calculating a distance matrix, and partitioning models into set of clusters based on calculated matrix.
US20080168070A1, presents a classification method for use over Internet, involving evaluation of multimedia artifacts (e.g. photographs) using selected classifiers to determine classification (tags). The semantic tagging is enhanced by applying only classifiers from selected ontologies based on scoring.
JP2008131170A defines an apparatus for generating knowledge metadata for use in choosing multimedia content. It specifies a generation unit that generates knowledge metadata relevant to a user, based on ontology with respect to information resource after storing new concept in the ontology.
The examples above elaborate on media classification involving ontologies in some way, but none of them presents a solution on how to connect specialized concepts in an ontology to numeric measurable observations in the media domain.